Deterministic chaos provides a novel framework for the analysis of irregular
time series. Traditionally, nonperiodic signals are modeled by linear
stochastic processes. But even very simple chaotic dynamical systems can
exhibit strongly irregular time evolution without random inputs. Chaos theory
offers completely new concepts and algorithms for time series analysis which
can lead to a thorough understanding of the signal. The book introduces a broad
choice of such concepts and methods, including phase space embeddings,
nonlinear prediction and noise reduction, Lyapunov exponents, dimensions and
entropies, as well as statistical tests for nonlinearity. Also related topics
like chaos control, wavelet analysis and pattern dynamics are
discussed. Applications range from high quality, strictly deterministic
laboratory data to short, noisy sequences which typically occur in medicine,
biology, geophysics or the social sciences. All material is discussed and
illustrated using real experimental data. For the main algorithms, sample
computer programs in C and FORTRAN are given. For the convenience of our
readers, the sample programs can also be downloaded from this server.
J. Stark says in UK Nonlinear News:

"I shall resume teaching the time series course next spring. Frankly, I am inclined to simply hold up a copy of Nonlinear Time Series
Analysis and tell my students to go away and read it, and then walk out of the lecture hall. Who needs a lecturer when a book this good is available?"